The named concepts and compositional operators present in natural language provide a rich source of information about the kinds of abstractions humans use to navigate the world. Can this information help us build better machine learning models? We’ll explore three different ways of using language to support learning: to provide structure to question answering models, fast training and improved generalization for reinforcement learners, and interpretability to general-purpose deep models.